Enterprises are increasingly capturing, storing, and mining a plethora of information related to communications with their customers. Often this information is stored and indexed within databases. Once the information is indexed, queries are developed on an as-needed basis to mine the information from the database for a variety of organizational goals: such as planning, analytics, reporting, etc.
Many times the information stored and indexed is created, mined, updated, and manipulated by application programs created by developers on behalf of analysts. These programs are referred to as user-defined functions (UDF's).
The information stored in the databases also provides enterprises with an opportunity to derive relationships or patterns from that information; the relationships and patterns can be transformed into functions that when supplied certain input variables produce projected output values, which the enterprises can rely on. Such scenarios are particularly useful in projecting the impact of sales given certain predefined expected or anticipated conditions. This approach often involves mathematical regression algorithms.
One issue with regression analysis is the large amounts of information typically needed to produce meaningful and reliable results. The traditional manner in which this is done is via a tabular UDF that requires all the data to be packed into a single row of a database table. This is taxing on the server having the table and on the users that use the server and are dependent on results of the tabular UDF.
Therefore, it can be seen that improved techniques for processing regression analysis in a relational database environment are needed.